UAV-based and Satellite based data fusion for plant/soil monitoring
Interest in image processing and learning-based classification is recommended
The fusion of UAV-based and satellite-based data has emerged as a powerful approach for plant health monitoring in agriculture. This innovative technique combines the advantages of both platforms, offering a comprehensive and scalable solution. Unmanned Aerial Vehicles (UAVs) can provide high-resolution, real-time data at a localized level, enabling precise identification of plant stress, disease, or nutrient deficiencies. On the other hand, satellites offer a broader coverage area and revisit frequency, capturing changes over larger agricultural regions. When integrated, these data sources create a holistic view of crop health, enabling farmers and researchers to make informed decisions, optimize resource allocation, and improve crop yields. This fusion of data from different sources has the potential to revolutionize precision agriculture and enhance food production sustainability.
During this study, we will work on the fusion of satellite and drone-based images and use learning-based strategies to extract the level of stress or health level. We can also concentrate on Plant or soil level study and seek for stress, nutrition, package, or growth. We will fix the argument of the study later based on the interests of the candidate.